"""Extract satellite embeddings from Google Earth Engine and map them to a grid.

Author: Tobias Hölzer
Date: October 2025
"""

import os
import warnings
from pathlib import Path
from typing import Literal

import cyclopts
import ee
import geemap
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
from rich import pretty, print, traceback
from rich.progress import track

# Filter out the GeoDataFrame.swapaxes deprecation warning
warnings.filterwarnings("ignore", message=".*GeoDataFrame.swapaxes.*", category=FutureWarning)

pretty.install()
traceback.install()
ee.Initialize(project="ee-tobias-hoelzer")

DATA_DIR = Path(os.environ.get("DATA_DIR", "../../data")) / "entropyc-rts"
EMBEDDINGS_DIR = DATA_DIR / "embeddings"
EMBEDDINGS_DIR.mkdir(parents=True, exist_ok=True)


cli = cyclopts.App(name="alpha-earth")


@cli.command()
def download(grid: Literal["hex", "healpix"], level: int, backup_intermediate: bool = False):
    """Extract satellite embeddings from Google Earth Engine and map them to a grid.

    Args:
        grid (Literal["hex", "healpix"]): The grid type to use.
        level (int): The grid level to use.
        backup_intermediate (bool, optional): Whether to backup intermediate results. Defaults to False.

    """
    gridname = f"permafrost_{grid}{level}"
    grid = gpd.read_parquet(DATA_DIR / f"grids/{gridname}_grid.parquet")

    for year in track(range(2017, 2025), total=8, description="Processing years..."):
        embedding_collection = ee.ImageCollection("GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL")
        embedding_collection = embedding_collection.filterDate(f"{year}-01-01", f"{year}-12-31")
        aggs = ["median", "stdDev", "min", "max", "mean", "p1", "p5", "p25", "p75", "p95", "p99"]
        bands = [f"A{str(i).zfill(2)}_{agg}" for i in range(64) for agg in aggs]

        def extract_embedding(feature):
            # Filter collection by geometry
            geom = feature.geometry()
            embedding = embedding_collection.filterBounds(geom).mosaic()
            # Get mean embedding value for the geometry
            mean_dict = embedding.reduceRegion(
                reducer=ee.Reducer.median()
                .combine(ee.Reducer.stdDev(), sharedInputs=True)
                .combine(ee.Reducer.minMax(), sharedInputs=True)
                .combine(ee.Reducer.mean(), sharedInputs=True)
                .combine(ee.Reducer.percentile([1, 5, 25, 75, 95, 99]), sharedInputs=True),
                geometry=geom,
            )
            # Add mean embedding values as properties to the feature
            return feature.set(mean_dict)

        # Process grid in batches of 100
        batch_size = 100
        all_results = []
        n_batches = len(grid) // batch_size
        for batch_num, batch_grid in track(
            enumerate(np.array_split(grid, n_batches)),
            description="Processing batches...",
            total=n_batches,
        ):
            # Convert batch to EE FeatureCollection
            eegrid_batch = ee.FeatureCollection(batch_grid.to_crs("epsg:4326").__geo_interface__)

            # Apply embedding extraction to batch
            eeegrid_batch = eegrid_batch.map(extract_embedding)
            df_batch = geemap.ee_to_df(eeegrid_batch)

            # Store batch results
            all_results.append(df_batch)

            # Save batch immediately to disk as backup
            if backup_intermediate:
                batch_filename = f"{gridname}_embeddings-{year}_batch{batch_num:06d}.parquet"
                batch_result = batch_grid.merge(df_batch[[*bands, "cell_id"]], on="cell_id", how="left")
                batch_result.to_parquet(EMBEDDINGS_DIR / f"{batch_filename}")

        # Combine all batch results
        df = pd.concat(all_results, ignore_index=True)
        embeddings_on_grid = grid.merge(df[[*bands, "cell_id"]], on="cell_id", how="left")
        embeddings_file = EMBEDDINGS_DIR / f"{gridname}_embeddings-{year}.parquet"
        embeddings_on_grid.to_parquet(embeddings_file)
        print(f"Saved embeddings for year {year} to {embeddings_file.resolve()}.")


@cli.command()
def combine_to_zarr(grid: Literal["hex", "healpix"], level: int):
    """Combine yearly embeddings parquet files into a single zarr store.

    Args:
        grid (Literal["hex", "healpix"]): The grid type to use.
        level (int): The grid level to use.

    """
    embs = gpd.read_parquet(DATA_DIR / "embeddings" / f"permafrost_{grid}{level}_embeddings-2017.parquet")
    # ? Converting cell IDs from hex strings to integers for xdggs compatibility
    cells = [int(cid, 16) for cid in embs.cell_id.to_list()]
    years = list(range(2017, 2025))
    aggs = ["median", "stdDev", "min", "max", "mean", "p1", "p5", "p25", "p75", "p95", "p99"]
    bands = [f"A{str(i).zfill(2)}" for i in range(64)]

    a = xr.DataArray(
        np.nan,
        dims=("year", "cell", "band", "agg"),
        coords={"year": years, "cell": cells, "band": bands, "agg": aggs},
    )
    # ? These attributes are needed for xdggs
    a.cell.attrs = {
        "grid_name": "h3" if grid == "hex" else "healpix",
        "level": level,
    }
    if grid == "healpix":
        a.cell.attrs["indexing_scheme"] = "nested"

    for year in track(years, total=len(years), description="Processing years..."):
        embs = gpd.read_parquet(DATA_DIR / "embeddings" / f"permafrost_{grid}{level}_embeddings-{year}.parquet")
        for band in bands:
            for agg in aggs:
                col = f"{band}_{agg}"
                a.loc[{"band": band, "agg": agg, "year": year}] = embs[col].to_list()

    zarr_path = EMBEDDINGS_DIR / f"permafrost_{grid}{level}_embeddings.zarr"
    a.to_zarr(zarr_path, consolidated=False, mode="w")
    print(f"Saved combined embeddings to {zarr_path.resolve()}.")


def main():  # noqa: D103
    cli()


if __name__ == "__main__":
    main()